Zerihun T., Firdissa E., Fekadu F., Kebede T., Mathewos A., Mohamed A., Mizan T.,

Muluken B., Yosef G., Alemayehu A., and Birhanu B.

WHEAT FOR FOOD SECURITY IN AFRICA CONFERENCE

OCTOBER 8 – 12, 2012, ADDIS ABABA, ETHIOPIA

Exploiting Yield Potential of Ethiopian Commercial Bread

Wheat (Triticum aestivum L.) Varieties Outside their

Original Recommended Domains

Presentation outline

Introduction

Objectives

Materials and Methods

Results and Discussion

Summary and Conclusions

Wheat in Ethiopia:

The 2nd largest producer in SSA

Bread & durum wheat are dominant species

Durum & emmer are indigenous and BW is a recent

introduction

National average yield reached a 2.0 t/ha from a 0.8 t/ha in

1980s, 4-6 t/ha on research fields

INTRODUCTION

s

Debre Zeit

Holetta

Haremaya

Sinana

Awassa

Sirinka Adet

D/Birhan Ambo

Areka

Werer

Mekele

Currently 15 research centers are involved in wheat research

Since 1974, > 45 BW varieties have been released

Fig. 1. Active Wheat Research Centers in the Ethiopia

Cont...

Major wheat producing regions of Ethiopia

Highlands & mid-altitudes

Out of 18 major AEZ, it is grown in more than eight AEZ.

The major wheat growing areas include:

South - Hadiya & Kambata

South-Eastern - Arsi & Bale

Central Highlands – Shoa

North & North-Western - Gojam, Gondar, Wello and Tigray

Several secondary areas of wheat production in the country

Cont...

Major constraint of wheat production in the country;

Traditional production system,

Biotic and abiotic factors,

Inadequate and timely supply of inputs,

Suboptimal use of recommended packages,

Socio-economic limitations (credit and infrastructure),

Lack of varieties for specific growing conditions

(drought, waterlogged and irrigation)

Shortage of widely adapted varieties and

Lack of durable resistance of the existing widely

adapted varieties (Kubsa)

Cont...

Objectives

To investigate yield potential of the existing commercial BW

varieties outside their recommended domains

Cont...

Experimental sites

30 locations in 2011 main cropping season

Testing sites represent highland, mid-altitude and low land

wheat growing zone

Conducted both on station and on farm

These locations have d/t soil types, altitude & mean annual RF

Experimental Materials

21 commercial rust resistant BW varieties used (Table 1)

MATERIALS AND METHODS

Table 1. List of BW varieties used for the study

No Variety Year of

Release Maturity (days) Altitude (m) Released Center

1 Danda’a 2010 110-145 2000-2600 KARC /EIAR

2 Kakaba 2010 90-120 1500-2200 KARC /EIAR

3 Hawii* 2000 105-125 1800-2200 KARC /EIAR

4 Tusie* 1997 125-130 2000-2500 KARC /EIAR

5 Pavon-76* 1982 120-135 750-2500 KARC /EIAR

6 ET-13A2* 1981 127-149 2200-2900 KARC /EIAR

7 K6295-4A 1980 128-131 1900-2400 KARC /EIAR

8 Shorima 2011 126-130 1800-2400 KARC /EIAR

9 Huluka 2011 133-140 2200-2800 KARC /EIAR

10 Digelu* 2005 100-120 2000-2600 KARC /EIAR

11 Sofumar* 2000 125-150 2300-2800 SARC/OARI

12 Mada-Walabu* 2000 100-125 2300-2800 SARC/OARI

13 Tay 2005 104-130 1900-2800 ADARC/ARARI

14 Senkegna 2005 105-125 1900-2800 ADARC/ARARI

15 Gasay 2007 118-127 1890-2800 ADARC/ARARI

16 Menze 2007 154 2800-3100 DBARC/ARARI

17 Bolo 2009 157 2580-3100 DBARC/ARARI

18 Alidoro 2007 118-180 2200-2900 HARC/EIAR

19 Dinknesh 2007 145 2400-3000 SRARC/ARARI

20 Tossa 2004 134-143 2400-3000 SRARC/ARARI

21 Kulkulu - - - HAROMAYA UNIVERSITY

Cont...

Experimental design, procedures and statistical analysis

Experimental design was RCBD with 2 replications

Each plot consisted of 6 rows of 2.5 x 1.2 m spacing

Fertilizer and other agronomic practices were applied as per

the recommendations of each study area

Data for grain yield was collected from central 4 rows

Statistical analysis;

AMMI (Gauch, 1988) and ASV (Purchase 1997)

Agrobase20 statistical software was used

Cont...

Result and Discussions

ENTRY NAME KULUMSA BEKOJI HROMAYA

UNIVERSITY HOLLETA AREKA DHERA

SIRINKA

(GERAGERA) ALAMATA

SINANA

(GORO)

1 Danda'a 4422.5 7280.0 3550.0 5090.0 5250.0 2802.5 2601.5 1886.3 3344.0

2 Kakaba 4505.0 7232.5 3572.0 4502.5 5375.0 3397.5 2765.8 2282.5 3195.8

3 Hawii 4832.5 6332.5 3538.5 4780.0 4500.0 2372.5 1989.0 2047.5 3543.3

4 Tusie 4170.0 6402.5 3537.0 5882.5 6000.0 2900.0 2328.3 1906.3 2795.3

5 Pavon-76 5175.0 5695.0 3449.0 5572.5 5125.0 3675.0 2680.8 1750.0 2156.0

6 ET-13A2 3552.5 5357.5 3464.5 5180.0 5500.0 3802.5 2769.3 1298.8 1278.0

7 K6295-4A 3810.0 5620.0 3441.0 5632.5 4500.0 2807.5 2268.0 1481.3 2042.8

8 Shorima 4830.0 7760.0 3528.5 6192.5 5500.0 2797.5 2873.5 1455.0 3153.5

9 Huluka 4695.0 7475.0 3527.3 6317.5 5875.0 3395.0 2718.8 1437.5 2815.8

10 Digelu 3892.5 7080.0 3717.5 5735.0 5625.0 2222.5 2419.3 1375.0 2645.5

11 Sofumar 3315.0 6180.0 3527.8 4445.0 5000.0 2655.0 2322.0 1987.6 2401.8

12

Mada-

walabu 5070.0 7112.5 3505.0 5387.5 5625.0 2252.5 2623.8 2078.8 2630.3

13 Tay 4670.0 6472.5 3752.0 5457.5 5500.0 3155.0 2502.8 1797.5 2359.3

14 Senkegna 4192.5 6475.0 3550.0 6070.0 5375.0 2455.0 2200.5 1315.0 1665.0

15 Gasay 4457.5 7157.5 3449.5 5562.5 6000.0 2860.0 2643.3 1897.5 2923.5

16 Menze 2962.5 6430.0 3669.0 4730.0 5000.0 2010.0 2172.5 1750.0 2860.0

17 Bolo 3632.5 6712.5 3522.0 5045.0 5875.0 1557.5 2198.0 1451.3 2199.8

18 Alidoro 4345.0 7232.5 3490.0 5147.5 5450.0 1717.5 2297.5 2040.0 2074.0

19 Dinknesh 4970.0 6660.0 3442.0 5205.0 4875.0 2170.0 2860.5 1675.0 2383.8

20 Tossa 5257.5 5845.0 3501.0 4252.5 4750.0 2910.0 2707.8 2341.3 1730.0

21 Kulkulu 4062.5 7037.5 3479.8 5605.0 5375.0 2655.0 3437.0 1736.3 2146.5

GRAND MEAN 4324.8 6645.2 3534.0 5323.5 5336.9 2693.8 2541.9 1761.4 2492.5

Table 4. Some representative locations mean GY (kg/ha) of 21 BW varieties

SV DF

SS Explained (%)

MS Total Treatment

Variation

G X E

Explained

E 29 95960294** 87 -

R(E) 30 483068.50 0.45 -

G 20 1444322.9** 0.90 -

G x E 580 406111.8** 7 -

IPCA 1 48 975186.4** - 19.87

IPCA 2 46 906490.3** - 17.70

IPCA 3 44 665528.1** - 12.43

IPCA 4 42 541742.3** - 9.66

IPCA 5 40 510653.9** - 8.67

IPCA 6 38 459208.7** - 7.41

IPCA 7 36 311213.1 - 4.76

IPCA Residual 286 160563.5 - 19.50

Residual 600 256045.355 4.78

Table 1. AMMI analysis of variance for GY of 21 BW varieties tested at 30

locations in 2011

Cont...

CV = 16.41% R-Squared = 0.95 LSD ( 5%)= 181.44

Fig 1. AMMI-1 biplot for grain yield of main effects against IPCA-1

MEAN GRAIN YIELD (Kg/ha)

7,000 6,000 5,000 4,000 3,000 2,000 1,000

IPC

A-1

30

25

20

15

10

5

0

-5

-10

-15

-20

-25

-30

Danda’a

Kakaba

Hawi

Menze

Bolo

Sofumer

Madawalabu

Gasay Kulkulu

Tusie Pavon-76

ET-13A

K6295-4A

Shorima

Huluka

Tay

Senkegna Alidoro

Dinkinesh

Tossa Digelu

Table 2. Mean GY, IPCA scores & ASV of 21 BW varieties tested at 30 locations

Entry Varieties Mean GY

(Kg/ha) Rank IPCA-1 IPCA-2 ASV Rank

1 Danda’a 3169.56 8 27.53 7.24 31.74 18

2 Kakaba 3258.48 4 17.93 -11.19 23.03 13

3 Hawii 2955.6 15 22.56 -19.37 31.88 19

4 Tusie 3019.19 13 -11.11 -8.47 15.08 8

5 Pavon-76 3216.76 6 -12.91 -22.85 27.06 15

6 ET-13A2 2903.69 18 -24.77 -8.68 29.13 17

7 K6295-4A 2891.73 19 -4.09 -18.49 19.05 10

8 Shorima 3347.98 1 -9.76 6.25 12.61 4

9 Huluka 3239.12 5 -26.9 24.37 38.81 21

10 Digelu 3120.87 9 -4.92 13.81 14.87 7

11 Sofumar 2866.28 21 8.33 -8.52 12.65 5

12 Mada-Walabu 3306.43 2 0.96 -6.63 6.72 1

13 Tay 3201.19 7 -14.9 13.01 21.19 12

14 Senkegna 2867.2 20 -6.78 0.59 7.64 2

15 Gasay 3277.38 3 3.96 15.98 16.58 9

16 Menze 2952.34 16 20.74 1.33 23.32 14

17 Bolo 3067.56 11 23.8 18.09 32.27 20

18 Alidoro 2994.25 14 -5.01 5.75 8.04 3

19 Dinknesh 2944.61 17 -7.56 -10.35 13.39 6

20 Tossa 3057.81 12 -3.49 -19.76 20.15 11

21 Kulkulu 3078.61 10 6.39 27.88 28.79 16

Cont...

Significant G x E interactions showed inconsistency in performance of

genotypes across locations

Top three best performing varieties for GY across locations were,

Shorima, Mada-walabu and Gasay

According to stability statistics;

Mada-walabu……….High yielder

Shorima ………...….. High yielder

Senkegna…….…..…..Low yielder

Alidoro ………………Low yielder

Some other varieties having high interactions with the environment &

they need to recommend in a specific locations are; Danda’a, Huluka,

Kakaba, Hawii and Bolo

Summary and Conclusions

Some other varieties with high yield were not stable across locations.

So, clustering of wheat growing environments across the country is

essential for the best use of the existing high yielding BW varieties.

This research also a good indicator for the utilization of the existing

technology in a wider environment for other crops.

For further observation of the year effect and to support the results of

this study, this experiment repeated in 2012 main cropping season.

Cont...